We report a scalable and cost-effective technology for generating and screening high-complexity customizable peptide sets. The peptides are made as peptide-cDNA fusions by in vitro transcription/translation from pools of DNA templates generated by microarray-based synthesis. This approach enables large custom sets of peptides to be designed in silico, manufactured cost-effectively in parallel, and assayed efficiently in a multiplexed fashion. The utility of our peptide-cDNA fusion pools was demonstrated in two activity-based assays designed to discover protease and kinase substrates. In the protease assay, cleaved peptide substrates were separated from uncleaved and identified by digital sequencing of their cognate cDNAs. We screened the 3,011 amino acid HCV proteome for susceptibility to cleavage by the HCV NS3/4A protease and identified all 3 known trans cleavage sites with high specificity. In the kinase assay, peptide substrates phosphorylated by tyrosine kinases were captured and identified by sequencing of their cDNAs. We screened a pool of 3,243 peptides against Abl kinase and showed that phosphorylation events detected were specific and consistent with the known substrate preferences of Abl kinase. Our approach is scalable and adaptable to other protein-based assays.
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ACS Cent Sci
December 2024
Department of Molecular Sciences and Nanosystems, Ca' Foscari University of Venice, Via Torino 155, 30172 Mestre, Italy.
Computational generation of cyclic peptide inhibitors using machine learning models requires large size training data sets often difficult to generate experimentally. Here we demonstrated that sequential combination of Random Forest Regression with the pseudolikelihood maximization Direct Coupling Analysis method and Monte Carlo simulation can effectively enhance the design pipeline of cyclic peptide inhibitors of a tumor-associated protease even for small experimental data sets. Further studies showed that such -evolved cyclic peptides are more potent than the best peptide inhibitors previously developed to this target.
View Article and Find Full Text PDFBiomed Pharmacother
December 2024
Department of Research, Mount Sinai Medical Center, Miami Beach, FL, USA. Electronic address:
Background: Excessive inflammation in sepsis causes microvascular dysfunction associated with organ dysfunction and high mortality. The present studies aimed to examine the therapeutic potential of linagliptin, a dipeptidyl peptidase-4 (DPP-4) inhibitor in a clinically relevant polymicrobial sepsis model in mice.
Methods: Sepsis was induced by cecal ligation and puncture (CLP).
Front Genet
December 2024
Institute of Biotechnology and Biomedicine, Universitat Autònoma de Barcelona, Cerdanyola del Vallès, Spain.
We present MMPred, a software tool that integrates epitope prediction and sequence alignment algorithms to streamline the computational analysis of molecular mimicry events in autoimmune diseases. Starting with two protein or peptide sets (e.g.
View Article and Find Full Text PDFChem Sci
December 2024
Department of Chemistry, School of Science, Westlake University 310030 Hangzhou Zhejiang Province China.
Sulfonium is an electrophilic and biocompatible group that is widely applied in synthetic chemistry on small molecules. However, there have been few developments of peptide or protein-based sulfonium tools. We recently reported sulfonium-mediated tryptophan crosslinking and developed NleSme2 (norleucine-dimethylsulfonium) peptides as dimethyllysine mimics that crosslink site-specific methyllysine readers.
View Article and Find Full Text PDFFront Endocrinol (Lausanne)
December 2024
Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University, Jinan, China.
Objective: This study aimed to explore the risk factors for gastrointestinal side effects (GISEs) in patients with type 2 diabetes mellitus (T2DM) during treatment with glucagon-like peptide-1 receptor agonists (GLP-1RAs) based on real-world data and to develop a prediction model for GLP-1RA-related GISEs.
Methods: A total of 855 patients who attended the First Affiliated Hospital of Shandong First Medical University from January 2020 to May 2023 were selected as the study participants, who were divided into the training set (598 cases) and the validation set (297 cases) using a simple random sampling method at a ratio of 7:3. The general information and biochemical indicators of the participants were collected to assess the risk factors for GLP-1RA-related GISEs, and multifactorial logistic regression analysis was used to obtain the best predictors.
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